The Manager of Data Engineering will oversee data engineering for an investment management firm, focusing on MS Fabric, Azure, and various data processing tasks.
Location: Boston, MA
Position Type: Hybrid
Hybrid Schedule: 4 days/week onsite
Contract Length: 7 months, contract-to-hire
Position Overview:
This role involves designing, building, and optimizing modern data platforms that support intelligent, data-driven experiences for global clients. Responsibilities include working across cloud, data engineering, and analytics disciplines to enable scalable ingestion, transformation, and storage of enterprise data within lakehouse and warehouse architectures. The position also requires close collaboration with architects, analysts, and product teams to deliver reliable, high-performing data solutions aligned with business goals.
Required Qualifications:
Position Type: Hybrid
Hybrid Schedule: 4 days/week onsite
Contract Length: 7 months, contract-to-hire
Position Overview:
This role involves designing, building, and optimizing modern data platforms that support intelligent, data-driven experiences for global clients. Responsibilities include working across cloud, data engineering, and analytics disciplines to enable scalable ingestion, transformation, and storage of enterprise data within lakehouse and warehouse architectures. The position also requires close collaboration with architects, analysts, and product teams to deliver reliable, high-performing data solutions aligned with business goals.
Required Qualifications:
- Hands-on experience or strong working knowledge of Microsoft Fabric, including its role in modern analytics and lakehouse architectures.
- Proven experience working in Azure for data ingestion and orchestration.
- Strong experience with Azure Data Factory (ADF) for pipeline development and scheduling.
- Experience building API-based data ingestion solutions.
- Solid understanding of data storage formats, including CSV, JSON, and Parquet.
- Experience designing and working with data warehouses and lakehouse architectures.
- Strong foundation in data modeling concepts for analytical workloads.
- Practical experience implementing medallion architecture patterns.
- Proficiency in PySpark for large-scale data transformations and optimization.
- Ability to write clean, maintainable, and well-documented data pipelines.
- Experience optimizing Spark jobs for performance and cost in cloud environments.
- Familiarity with data governance, data quality, or observability practices in large-scale data platforms.
- Experience collaborating with analytics, data science, or AI teams on production-grade data solutions.
- Exposure to agile delivery models and working in cross-functional, client-facing teams.
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